2021 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO)最新文献

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A Quantifiable Stratification Strategy for Tidy-up in Service Robotics 服务机器人中可量化的整理分层策略

2021 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO) Pub Date : 2021-07-08 DOI: 10.1109/ARSO51874.2021.9542842

Zhi Yan, Nathan Crombez, J. Buisson, Y. Ruichek, T. Krajník, Li Sun

{"title":"A Quantifiable Stratification Strategy for Tidy-up in Service Robotics","authors":"Zhi Yan, Nathan Crombez, J. Buisson, Y. Ruichek, T. Krajník, Li Sun","doi":"10.1109/ARSO51874.2021.9542842","DOIUrl":"https://doi.org/10.1109/ARSO51874.2021.9542842","url":null,"abstract":"This paper addresses the problem of tidying up a living room in a messy condition with a service robot (i.e. domestic mobile manipulator). One of the key issues in completing such a task is how to continuously select the object to grasp and take it to the delivery area, especially when the robot works in constrained and partially observable environments. In this paper, we propose a quantifiable stratification method that allows the robot to find feasible action plans according to different configurations of objects-deposits, in order to smoothly deliver the objects to the target deposits. Specifically, it leverages a finite-state machine obeying the principle of Occam's razor (called O- FSM), which is designed to integrate arbitrary user-defined action plans typically ranging from simple to complex. Instead of considering a sophisticated model for the ever-changing objects-deposits configuration in the tidy-up task, we empower the robot to make simple yet effective decisions based on its current faced configuration under a generalized framework. Through scenario planning and simulation experiments with the explicitly designed test cases based on the real robot and the real competition scene, the effectiveness of our method is illustrated.","PeriodicalId":156296,"journal":{"name":"2021 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123502755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

An Efficient Multi-Vehicle Routing Strategy for Goods Delivery Services 货物递送服务的高效多车辆路径策略

2021 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO) Pub Date : 2021-04-30 DOI: 10.36227/TECHRXIV.14481702.V1

Duy Le, Yi Men, Yun-Jhen Luo, Yixuan Zhou, Linh Nguyen

{"title":"An Efficient Multi-Vehicle Routing Strategy for Goods Delivery Services","authors":"Duy Le, Yi Men, Yun-Jhen Luo, Yixuan Zhou, Linh Nguyen","doi":"10.36227/TECHRXIV.14481702.V1","DOIUrl":"https://doi.org/10.36227/TECHRXIV.14481702.V1","url":null,"abstract":"The paper addresses the problem of efficiently planning routes for multiple ground vehicles used in goods delivery services. Given popularity of today's e-commerce, particularly under the COVID-19 pandemic conditions, goods delivery services have been booming than ever, dominated by small-scaled (electric) bikes and promised by autonomous vehicles. However, finding optimal routing paths for multiple delivery vehicles operating simultaneously in order to minimize transportation cost is a fundamental but challenging problem. In this paper, it is first proposed to exploit the mixed integer programming paradigm to model the delivery routing optimization problem (DROP) for multiple simultaneously-operating vehicles given their energy constraints. The routing optimization problem is then solved by the multi-chromosome genetic algorithm, where the number of delivery vehicles can be optimized. The proposed approach was evaluated in a realworld experiment in which goods were expected to be delivered from a depot to 26 suburb locations in Canberra, Australia. The obtained results demonstrate effectiveness of the proposed algorithm.","PeriodicalId":156296,"journal":{"name":"2021 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128469401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

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